Image recognition is a technique of identifying a subject in an image by extracting feature amount data from the image and comparing that data with feature amount data of a known object registered in advance in a database. Image recognition is applied in various fields, such as personal authentication or individual identification by means of a biometric image, monitoring systems that detect intruders or suspicious items, work inspection in a production line or the like, identification of passersby and passing vehicles in transportation infrastructure, and the like.
Since an image captured using a camera is used in image recognition, it is unavoidable that variation will appear in the extracted feature amount due to the shooting conditions at that time (e.g., the state of the object (in the case of a face, the orientation, expression, presence of accessories, make-up, hairstyle, etc.), the lighting state, etc.). In view of this, as methods of increasing robustness with respect to the differences in imaging conditions so as to improve recognition accuracy, a method is commonly employed in which multiple pieces of feature amount data extracted from different images are registered for the same object. In other words, it is desirable to increase variation in the feature amount data registered in the database in order to improve the accuracy of image recognition.
However, it does not mean simply increasing the amount of registered feature amount data. This is because there is a limit to the number of pieces of feature amount data that can be registered in the database (or with respect to each object) due to constraints on the storage capacity of the database and on the program. That is to say, in order to be able to perform image recognition at a higher level of accuracy with a limited amount of data, optimizing the feature amount data that is to be registered is viewed as important in practical use.
JP 2002-259980A and JP 2012-242891A are examples of conventional technology related to registration and updating of feature amount data. JP 2002-259980A discloses an idea in which round-robin comparison of registration candidate data and registered data is performed, and data with a higher priority ranking is stored in the database as new registration data. Also, JP 2012-242891A discloses an idea in which comparison of registration candidate data and registered data is performed, and only registration candidate images whose degree of similarity with respect to the registered data is neither too high nor too low are presented to the user, thus making it easier for the user to sift through the registration candidates. However, in JP 2002-259980A, no method for determining the priority ranking is specifically disclosed, and it is unclear how to realize the selection of feature amount data that is to be registered and how to realize updating of the database. Also, the purpose of the method disclosed in JP 2012-242891A is to assist operations and determination performed by the user, and no specific method for automatically updating the database is provided.
JP 2002-259980A and JP 2012-242891A are examples of background art.
The present invention has been made in view of the foregoing situation, and it is an object of the invention to provide a technique for automatically optimizing feature amount data that is to be registered in the database, so that a favorable recognition accuracy is obtained.
In order to achieve the above-described object, the present invention uses a configuration in which registration, disposal, replacement, and the like of feature amount data is determined automatically, such that variation in pieces of feature amount data registered for the same registered object is maximized.